Update app.py

app.py

@@ -1,299 +1,316 @@
 import gradio as gr
 import google.generativeai as genai
 from PIL import Image
+import io
+import base64
+from typing import Optional, List, Tuple
 import json
-from typing import List, Dict, Any
-import os
-from datetime import datetime
 
-# Configure Gemini API
-# You'll need to set this as an environment variable in Hugging Face Spaces
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-
-class MedicalAssistant:
-    def __init__(self):
-        if not GOOGLE_API_KEY:
-            raise ValueError("Please set GOOGLE_API_KEY environment variable")
-        
-        genai.configure(api_key=GOOGLE_API_KEY)
+# Configure Gemini API using HuggingFace secrets
+def setup_gemini():
+    """Setup Gemini API with HuggingFace secrets"""
+    try:
+        # Access API key from HuggingFace secrets
+        import os
+        api_key = None
         
-        # Use Gemini Pro Vision for multimodal capabilities
-        self.model = genai.GenerativeModel('gemini-1.5-pro-latest')
+        # Try multiple ways to access the secret
+        if hasattr(gr, 'secrets'):
+            api_key = gr.secrets.get("GEMINI_API_KEY")
         
-        # Medical analysis prompt template
-        self.medical_prompt_template = """
-You are an expert medical AI assistant with comprehensive training in medical imaging, diagnostics, and clinical decision support. Your analysis should be thorough, evidence-based, and clinically relevant.
+        if not api_key:
+            # Fallback to environment variable for local testing
+            api_key = os.getenv("GEMINI_API_KEY")
+            
+        if not api_key:
+            raise ValueError("GEMINI_API_KEY not found in secrets")
+            
+        genai.configure(api_key=api_key)
+        return genai.GenerativeModel('gemini-1.5-pro')
+    except Exception as e:
+        print(f"Error setting up Gemini: {e}")
+        return None
+
+def create_medical_prompt(patient_data: dict, complaints: str, has_image: bool = False) -> str:
+    """Create an optimized prompt for Gemini's medical analysis"""
+    
+    base_prompt = """You are an expert medical AI assistant with extensive training in medical imaging, diagnostics, and clinical decision support. Please provide a comprehensive medical analysis based on the provided information.
 
 **IMPORTANT DISCLAIMERS:**
-- This analysis is for educational and assistive purposes only
-- It does not replace professional medical consultation
-- All findings should be verified by qualified healthcare providers
+- This is for educational and decision support purposes only
+- Always requires human physician review and validation
+- Not a substitute for professional medical judgment
 - Emergency cases require immediate medical attention
 
-**PATIENT INFORMATION:**
-{patient_info}
-
-**CHIEF COMPLAINTS:**
-{complaints}
-
-**MEDICAL IMAGES PROVIDED:**
-{image_count} image(s) uploaded for analysis
+**PATIENT INFORMATION:**"""
+    
+    # Add patient demographics
+    if patient_data.get('age'):
+        base_prompt += f"\n- Age: {patient_data['age']} years"
+    if patient_data.get('gender'):
+        base_prompt += f"\n- Gender: {patient_data['gender']}"
+    if patient_data.get('weight'):
+        base_prompt += f"\n- Weight: {patient_data['weight']} kg"
+    if patient_data.get('height'):
+        base_prompt += f"\n- Height: {patient_data['height']} cm"
+    
+    # Add medical history
+    if patient_data.get('medical_history'):
+        base_prompt += f"\n- Medical History: {patient_data['medical_history']}"
+    if patient_data.get('medications'):
+        base_prompt += f"\n- Current Medications: {patient_data['medications']}"
+    if patient_data.get('allergies'):
+        base_prompt += f"\n- Allergies: {patient_data['allergies']}"
+    
+    # Add chief complaints
+    base_prompt += f"\n\n**CHIEF COMPLAINTS AND SYMPTOMS:**\n{complaints}"
+    
+    # Add image analysis instruction if image is provided
+    if has_image:
+        base_prompt += "\n\n**MEDICAL IMAGE ANALYSIS:**\nPlease analyze the provided medical image(s) in detail, describing all visible findings, abnormalities, and relevant anatomical structures."
+    
+    # Analysis framework
+    base_prompt += """
 
-**TASK:**
-Please provide a comprehensive medical analysis including:
+**PLEASE PROVIDE YOUR ANALYSIS IN THE FOLLOWING STRUCTURED FORMAT:**
 
-1. **IMAGE ANALYSIS** (if images provided):
-   - Systematic description of visible findings
-   - Anatomical structures identified
-   - Any abnormalities, lesions, or pathological changes
-   - Image quality and limitations assessment
+## 1. CLINICAL ASSESSMENT
+- **Primary Findings:** [Key observations from history and/or imaging]
+- **Vital Signs Assessment:** [If provided, comment on vital signs]
+- **Physical Examination Notes:** [Relevant physical findings mentioned]
 
-2. **CLINICAL CORRELATION:**
-   - How imaging findings relate to patient symptoms
-   - Differential diagnoses based on presentation
-   - Risk factors and red flags identified
+## 2. DIFFERENTIAL DIAGNOSIS
+- **Most Likely Diagnosis:** [Primary consideration with reasoning]
+- **Alternative Diagnoses:** [Other possibilities to consider]
+- **Ruling Out:** [Important conditions to exclude]
 
-3. **DIFFERENTIAL DIAGNOSES:**
-   - List 3-5 most likely diagnoses with reasoning
-   - Include probability assessment if possible
-   - Consider both common and critical conditions
+## 3. IMAGING ANALYSIS (if applicable)
+- **Technical Quality:** [Assessment of image quality]
+- **Anatomical Structures:** [Normal structures identified]
+- **Abnormal Findings:** [Any pathological changes]
+- **Measurements:** [Relevant measurements if applicable]
 
-4. **RECOMMENDED INVESTIGATIONS:**
-   - Additional imaging studies if needed
-   - Laboratory tests to consider
-   - Specialist consultations advised
+## 4. RECOMMENDED INVESTIGATIONS
+- **Laboratory Tests:** [Suggested blood work, cultures, etc.]
+- **Imaging Studies:** [Additional imaging if needed]
+- **Specialized Tests:** [ECG, spirometry, biopsy, etc.]
 
-5. **IMMEDIATE CONCERNS:**
-   - Any findings requiring urgent attention
-   - Red flags or emergency indicators
+## 5. TREATMENT RECOMMENDATIONS
+- **Immediate Management:** [Urgent interventions if needed]
+- **Medication Suggestions:** [Pharmacological interventions]
+- **Non-Pharmacological:** [Lifestyle, physical therapy, etc.]
+- **Follow-up:** [Monitoring and reassessment timeline]
 
-6. **MANAGEMENT SUGGESTIONS:**
-   - Initial treatment considerations
-   - Follow-up recommendations
-   - Patient education points
+## 6. RED FLAGS & URGENCY
+- **Emergency Indicators:** [Signs requiring immediate attention]
+- **Warning Signs:** [Symptoms to monitor]
+- **When to Seek Help:** [Clear escalation criteria]
 
-7. **LIMITATIONS & CONSIDERATIONS:**
-   - Limitations of the current analysis
-   - Information gaps that need addressing
-   - Quality of provided data
+## 7. PATIENT EDUCATION
+- **Condition Explanation:** [Simple explanation of likely diagnosis]
+- **Lifestyle Modifications:** [Relevant lifestyle advice]
+- **Prognosis:** [Expected outcome with treatment]
 
-Please structure your response clearly with appropriate medical terminology while ensuring it's understandable. Use evidence-based medicine principles and cite relevant clinical guidelines where applicable.
+**Remember:** This analysis is for educational and decision support purposes. Always correlate with clinical judgment and seek appropriate medical consultation."""
 
-Remember to maintain a professional, empathetic tone and emphasize the importance of professional medical consultation for definitive diagnosis and treatment.
-"""
+    return base_prompt
 
-    def format_patient_info(self, age, gender, medical_history, current_medications, allergies, vital_signs):
-        """Format patient information for the prompt"""
-        info = []
-        if age:
-            info.append(f"Age: {age}")
-        if gender:
-            info.append(f"Gender: {gender}")
-        if medical_history:
-            info.append(f"Medical History: {medical_history}")
-        if current_medications:
-            info.append(f"Current Medications: {current_medications}")
-        if allergies:
-            info.append(f"Allergies: {allergies}")
-        if vital_signs:
-            info.append(f"Vital Signs: {vital_signs}")
+def analyze_medical_case(image_files, age, gender, weight, height, medical_history, 
+                        medications, allergies, complaints):
+    """Analyze medical case using Gemini AI"""
+    
+    model = setup_gemini()
+    if not model:
+        return "❌ Error: Could not initialize Gemini AI. Please check API key in secrets."
+    
+    try:
+        # Prepare patient data
+        patient_data = {
+            'age': age,
+            'gender': gender,
+            'weight': weight,
+            'height': height,
+            'medical_history': medical_history,
+            'medications': medications,
+            'allergies': allergies
+        }
         
-        return "\n".join(info) if info else "No patient information provided"
-
-    def analyze_medical_case(self, images, age, gender, medical_history, 
-                           current_medications, allergies, vital_signs, complaints):
-        """Analyze medical case with Gemini"""
-        try:
-            # Format patient information
-            patient_info = self.format_patient_info(
-                age, gender, medical_history, current_medications, 
-                allergies, vital_signs
-            )
-            
-            # Prepare the prompt
-            prompt = self.medical_prompt_template.format(
-                patient_info=patient_info,
-                complaints=complaints if complaints else "No specific complaints provided",
-                image_count=len(images) if images else 0
-            )
-            
-            # Prepare content for Gemini
-            content = [prompt]
-            
-            # Add images if provided
-            if images:
-                for img_path in images:
-                    try:
-                        img = Image.open(img_path)
-                        content.append(img)
-                    except Exception as e:
-                        return f"Error loading image {img_path}: {str(e)}"
-            
-            # Generate response
-            response = self.model.generate_content(content)
-            
-            # Add timestamp and disclaimer
-            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-            output = f"**Analysis Generated on: {timestamp}**\n\n"
-            output += "⚠️ **MEDICAL DISCLAIMER**: This is an AI-generated analysis for educational purposes only. "
-            output += "It is not a substitute for professional medical advice, diagnosis, or treatment. "
-            output += "Always seek the advice of qualified healthcare providers.\n\n"
-            output += "---\n\n"
-            output += response.text
-            
-            return output
-            
-        except Exception as e:
-            return f"Error during analysis: {str(e)}\n\nPlease check your API key and inputs."
+        # Validate input
+        if not complaints.strip():
+            return "❌ Please provide patient complaints and symptoms."
+        
+        # Create prompt
+        has_images = image_files is not None and len(image_files) > 0
+        prompt = create_medical_prompt(patient_data, complaints, has_images)
+        
+        # Prepare content for Gemini
+        content = [prompt]
+        
+        # Process images if provided
+        if has_images:
+            for img_file in image_files:
+                try:
+                    # Open and process image
+                    image = Image.open(img_file.name)
+                    # Convert to RGB if necessary
+                    if image.mode != 'RGB':
+                        image = image.convert('RGB')
+                    content.append(image)
+                except Exception as e:
+                    return f"❌ Error processing image: {str(e)}"
+        
+        # Generate response
+        response = model.generate_content(content)
+        
+        if response.text:
+            return f"""# 🏥 Medical AI Analysis Report
 
-# Initialize the assistant
-assistant = MedicalAssistant()
+{response.text}
 
-# Create Gradio interface
-def process_medical_case(images, age, gender, medical_history, 
-                        current_medications, allergies, vital_signs, complaints):
-    """Process medical case through Gradio interface"""
-    
-    # Validate inputs
-    if not complaints and not images:
-        return "Please provide either medical images or patient complaints for analysis."
-    
-    # Get image paths if images uploaded
-    image_paths = []
-    if images:
-        if isinstance(images, list):
-            image_paths = [img.name if hasattr(img, 'name') else img for img in images]
+---
+**⚠️ IMPORTANT MEDICAL DISCLAIMER:**
+This AI analysis is for educational and decision support purposes only. It should not replace professional medical judgment, clinical examination, or established medical protocols. Always consult with qualified healthcare professionals for medical decisions. In emergency situations, seek immediate medical attention."""
         else:
-            image_paths = [images.name if hasattr(images, 'name') else images]
-    
-    # Run analysis
-    result = assistant.analyze_medical_case(
-        image_paths, age, gender, medical_history,
-        current_medications, allergies, vital_signs, complaints
-    )
-    
-    return result
+            return "❌ Could not generate analysis. Please try again."
+            
+    except Exception as e:
+        return f"❌ Error during analysis: {str(e)}"
 
-# Create Gradio interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 🏥 Medical AI Assistant powered by Gemini
+def create_interface():
+    """Create Gradio interface for the medical assistant"""
+    
+    with gr.Blocks(title="🏥 Medical AI Assistant", theme=gr.themes.Soft()) as interface:
         
-        This AI assistant analyzes medical images and patient data to provide clinical insights.
-        Upload medical images and enter patient information for comprehensive analysis.
+        gr.Markdown("""
+        # 🏥 Medical AI Assistant with Gemini
         
-        **Note**: Ensure you have set the GOOGLE_API_KEY environment variable in your Hugging Face Space settings.
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 📋 Patient Information")
-            age = gr.Number(label="Age", precision=0)
-            gender = gr.Dropdown(
-                label="Gender", 
-                choices=["Male", "Female", "Other", "Prefer not to say"],
-                value="Prefer not to say"
-            )
-            medical_history = gr.Textbox(
-                label="Medical History",
-                placeholder="Previous diagnoses, surgeries, chronic conditions...",
-                lines=3
-            )
-            current_medications = gr.Textbox(
-                label="Current Medications",
-                placeholder="List all current medications and dosages...",
-                lines=2
-            )
-            allergies = gr.Textbox(
-                label="Allergies",
-                placeholder="Drug allergies, food allergies, etc...",
-                lines=2
-            )
-            vital_signs = gr.Textbox(
-                label="Vital Signs",
-                placeholder="BP: 120/80, HR: 72, Temp: 98.6°F, etc...",
-                lines=2
-            )
+        **Advanced Medical Case Analysis and Decision Support**
+        
+        Upload medical images, enter patient data, and get comprehensive AI-powered medical insights using Google's Gemini AI, specially trained on medical data and imaging.
+        
+        ⚠️ **For Healthcare Professionals Only** - This tool is designed for medical professionals and should not replace clinical judgment.
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("### 📁 Medical Images")
+                image_files = gr.File(
+                    label="Upload Medical Images",
+                    file_types=[".jpg", ".jpeg", ".png", ".bmp", ".tiff"],
+                    file_count="multiple",
+                    height=200
+                )
+                
+                gr.Markdown("### 👤 Patient Demographics")
+                with gr.Row():
+                    age = gr.Number(label="Age (years)", value=None, minimum=0, maximum=120)
+                    gender = gr.Dropdown(
+                        label="Gender",
+                        choices=["Male", "Female", "Other", "Prefer not to say"],
+                        value=None
+                    )
+                
+                with gr.Row():
+                    weight = gr.Number(label="Weight (kg)", value=None, minimum=0)
+                    height = gr.Number(label="Height (cm)", value=None, minimum=0)
+                
+                gr.Markdown("### 📋 Medical History")
+                medical_history = gr.Textbox(
+                    label="Past Medical History",
+                    placeholder="Previous diagnoses, surgeries, chronic conditions...",
+                    lines=3
+                )
+                
+                medications = gr.Textbox(
+                    label="Current Medications",
+                    placeholder="List current medications, dosages, frequency...",
+                    lines=2
+                )
+                
+                allergies = gr.Textbox(
+                    label="Known Allergies",
+                    placeholder="Drug allergies, food allergies, environmental...",
+                    lines=2
+                )
             
-        with gr.Column(scale=1):
-            gr.Markdown("### 🏥 Clinical Data")
-            complaints = gr.Textbox(
-                label="Chief Complaints & Symptoms",
-                placeholder="Describe main symptoms, duration, severity, associated factors...",
-                lines=6
-            )
-            images = gr.File(
-                label="Medical Images",
-                file_count="multiple",
-                file_types=["image"],
-                type="filepath"
-            )
-            gr.Markdown(
-                """
-                **Supported formats**: X-rays, CT scans, MRI, ultrasound, clinical photos
+            with gr.Column(scale=1):
+                gr.Markdown("### 🩺 Chief Complaints & Symptoms")
+                complaints = gr.Textbox(
+                    label="Patient Complaints and Symptoms",
+                    placeholder="""Describe the patient's main complaints, symptoms, and clinical presentation:
+
+• Chief complaint and duration
+• Associated symptoms
+• Severity and progression
+• Triggering factors
+• Previous treatments tried
+• Vital signs (if available)
+• Physical examination findings
+• Any relevant recent changes""",
+                    lines=12
+                )
                 
-                **Privacy Note**: Ensure all images are properly anonymized
-                """
-            )
-    
-    submit_btn = gr.Button("🔬 Analyze Case", variant="primary", size="lg")
-    
-    output = gr.Markdown(label="Analysis Results")
-    
-    # Examples
-    gr.Examples(
-        examples=[
-            [
-                None,  # images
-                45,    # age
-                "Male", # gender
-                "Hypertension for 5 years, Type 2 diabetes", # medical history
-                "Metformin 500mg BD, Amlodipine 5mg OD", # medications
-                "None known", # allergies
-                "BP: 140/90, HR: 78, RR: 16", # vital signs
-                "Chest pain for 2 days, radiating to left arm, associated with shortness of breath" # complaints
+                analyze_btn = gr.Button(
+                    "🔍 Analyze Medical Case",
+                    variant="primary",
+                    size="lg"
+                )
+        
+        gr.Markdown("### 📊 AI Medical Analysis")
+        output = gr.Markdown(label="Analysis Results")
+        
+        # Examples section
+        gr.Markdown("""
+        ### 💡 Example Use Cases
+        
+        **Radiology:** Upload X-rays, CT scans, MRIs for detailed imaging analysis
+        **Dermatology:** Skin lesion photos with patient history
+        **Cardiology:** ECGs, echocardiogram images with cardiac symptoms
+        **Pathology:** Microscopic images with clinical context
+        **Emergency Medicine:** Multi-modal case analysis with urgency assessment
+        """)
+        
+        # Set up the analysis function
+        analyze_btn.click(
+            fn=analyze_medical_case,
+            inputs=[
+                image_files, age, gender, weight, height,
+                medical_history, medications, allergies, complaints
             ],
-            [
-                None,
-                28,
-                "Female",
-                "Migraine headaches",
-                "None",
-                "Penicillin - rash",
-                "BP: 110/70, HR: 68",
-                "Severe headache, photophobia, nausea for 6 hours"
-            ]
-        ],
-        inputs=[images, age, gender, medical_history, current_medications, 
-                allergies, vital_signs, complaints]
-    )
-    
-    submit_btn.click(
-        fn=process_medical_case,
-        inputs=[images, age, gender, medical_history, current_medications, 
-                allergies, vital_signs, complaints],
-        outputs=output
-    )
-    
-    gr.Markdown(
-        """
+            outputs=output
+        )
+        
+        gr.Markdown("""
         ---
-        ### ⚕️ Important Information:
-        - This tool is for educational and assistive purposes only
-        - Always consult qualified healthcare professionals for medical decisions
-        - Ensure patient privacy and HIPAA compliance when using this tool
-        - Report any issues or feedback to improve the system
+        ### 🔧 Setup Instructions for HuggingFace Deployment
         
-        ### 🔧 Setup Instructions for Hugging Face:
-        1. Create a Space on Hugging Face
-        2. Add your Google API key as a secret: GOOGLE_API_KEY
-        3. Install requirements: `google-generativeai gradio pillow`
-        4. Deploy this code
-        """
-    )
+        1. **Add API Key to Secrets:**
+           - Go to your HuggingFace Space settings
+           - Add a new secret: `GEMINI_API_KEY` = `your_gemini_api_key`
+           - Get your API key from: https://makersuite.google.com/app/apikey
+        
+        2. **Required Dependencies (requirements.txt):**
+        ```
+        gradio
+        google-generativeai
+        Pillow
+        ```
+        
+        ### ⚠️ Important Medical Disclaimers
+        - This AI assistant is for educational and decision support purposes only
+        - Always requires validation by qualified healthcare professionals
+        - Not a substitute for clinical examination and medical judgment
+        - Emergency cases require immediate medical attention
+        - Users assume responsibility for clinical decisions
+        """)
+    
+    return interface
 
-# Launch the app
+# Create and launch the interface
 if __name__ == "__main__":
-    demo.launch()
+    demo = create_interface()
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860
+    )